The following information was taken from meeting notes (meetings on 6/4/2015 and 5/20/2015)
In Ag & Life Science, I suspect the computer allocation has decreased due to Moore's law (it's cheaper to get computers that can process genetic data now than it was in 2005). Engineering allocations seem to be somewhat department-related (clustering of green and blue values, in particular). The business school seems remarkably standardized - the amount decreased in 2011-2013, but otherwise has been at approximately the same level (with extremely low variance).
Lab space and lab equipment are obviously only relevant for some colleges. The proportion and raw amount of funding for lab space and equipment in LAS has decreased, while the amount of funding in engineering and human sciences has increased (though the proportion of the funding package has stayed reasonably consistent).
The recession seems to have hit graduate student support fairly hard (relative to other expenses). Looking at the gross amount of funding allocated for graduate students, we see that startup packages in engineering and human sciences include more support for graduate students than in the past (this is particularly dramatic in human sciences, though the amount of funding is still overall lower than in engineering). In the other colleges, the amount of support for graduate students has stayed relatively consistent over the past 10 years.
Summer funding seems to be fairly common in some areas (business, some of LAS) and almost unheard of in others (design, vet med).
Moving expenses amounts seem to be reasonably consistent across colleges. The business college seems to once again have a standard package that may change year to year with occasional negotiation. The same may be true of the vet med college.
Datasets from OSPA contain information on proposals and awards from 2005-2010 and 2010-2015 (4 total databases).
The following sections are intended to explore some of the limitations of the Proposal and Award datasets. They are not essential for understanding the subsequent analysis. ### Proposals
Some categories of proposals occur in FY06-FY10 but not in FY11-FY16. We do not have proposal type data for FY06-FY10. This would be useful to have, but does not seem to be present in the database.Similarly, we don't have information on the funding type for FY11-FY16. It may be useful to exclude Master Agreements, etc. from the data if no similar category exists in FY11-FY16.
Other differences between the two proposal datasets are unsurprising, such as that there are no pending proposals from FY06-FY10. Proposals are roughly evenly distributed over time, with a slight spike that corresponds to April - July 2009 (presumably, the American Recovery and Reinvestment Act proposals). Next, we consider the timeline for each proposal. Proposals have a submission date, a start date (presumably, when funding would begin), and an end date (when funding would cease).There is clearly some periodicity in the funding cycle (end dates, in particular) likely caused by differences between the academic year, government fiscal year, and calendar year.
Examining this from a slightly different perspective, we consider the difference between proposal submission dates and the start and end dates in those proposals.17% of proposals appear to start before they are submitted, and 0.28% also end before they are submitted.
Looking at the funding duration directly, we see that proposals tend to provide funding over 1-5 years, though very few do extend 10 or more years into the future. A few proposals extend 100 years into the future; this situation occurs when an amount of money is donated for some purpose and can be used at any point.The length of funding also differs between the two datasets as a result of the difference in start date calculations.
Award status differs between the two files as well; this is to be expected (more Active awards should be in the FY11-FY16 data, for instance). "Final" and "Executed" awards are present in FY11-FY16; I am currently tracking down what those statuses imply. Finally, we explore the amount of grant funding by year. Grants spanning many years are allocated to the midpoint of the funding range.We'll begin by looking at the timeline - how many years at ISU are necessary before grant applications are successful? We split this by college and faculty rank (as we'd expect that full professors who are hired should be able to command grant money sooner than new faculty). Subsequent graphs show data from hired professors (i.e. not adjuncts, affiliates, lecturers, or clinicians).
The most noticeable difference between colleges is that in engineering, assistant professors get grants about five years after they are hired, and it is very rare for professors at that stage in their careers not to get grant funding. This trend is present (though less pronounced) in Ag & Life Sciences and Human Sciences as well, though some assistant professors in those colleges seem to get funding much earlier in their careers (perhaps because of large grants given to groups of professors). In Liberal Arts and Sciences, grant funding does seem to increase with experience for some hires, but perhaps because LAS includes both arts (which are less grant-reliant) and sciences, this trend is less pronounced than in other colleges.
In order to examine the effect of startup funding on grant receipts over a career of variable duration, we will examine the distribution of startup funding by college, categorizing startup costs by the quartile (calculated for the hire's college). Below, vertical lines mark these quantiles
Using these quantile calculations, we then can examine total grants received as explained by years since hired.
It is important to note several caveats at this point: first, hires are not separated out by faculty rank or position, so hires whose primary responsibilities include administrative work may command large startup packages but bring in relatively little grant funding. Additionally, different colleges have different funding structures: LAS, for instance, may need to provide lab startup funding for science based departments, but would not have to provide this funding for new hires in English or History. Finally, the x-axis shows years since hire, which does not necessarily translate to years at ISU (faculty hired in 2005 may have left in 2011 after receiving tenure, for example).
Linear regression lines are provided here as (extremely) rough approximations; it may be useful to examine the right-most endpoint (rather than the entire line) as a prediction of total grant money received after 10 years at ISU.
With these caveats in mind,